Lamini AI, a specialist in LLM tuning for enterprise customers, has introduced a novel Memory Tuning method designed to drastically enhance the performance of large language models (LLMs). According to the company, Lamini Memory Tuning achieves a new high of 95% accuracy by reducing hallucinations by 90%.
Tackling Factual Inaccuracies in LLMs
General-purpose LLMs often struggle with precise facts, as they aim to minimize average errors across a range of examples. Traditional approaches such as Prompting and Retrieval-Augmented Generation (RAG), using external data sources to improve the accuracy and relevance of generated content, frequently fail to completely eliminate hallucinations. While these methods may increase the probability of correct responses, they still allow for nearly correct yet ultimately incorrect outputs.
Lamini AI´s newly developed Memory Tuning addresses the ongoing issue of factual inaccuracies in LLMs. By fine-tuning millions of specialized adapters, such as Low-Rank Adapters (LoRAs), on any open-source LLM, the technique embeds accurate information directly into the model. As a result, the model can access only the most relevant data during inference, boosting both precision and efficiency.
Lamini's Memory Tuning leverages a massive mixture of memory experts (MoMEs), akin to specialized indices in information retrieval. These experts are fine-tuned to recall specific facts precisely and are chosen dynamically during inference. This approach maintains the model's capability to generate coherent sentences while ensuring near-perfect recall of important facts. The result is a sparsely activated model that can scale to an enormous number of parameters at a fixed computational inference cost.
The technique also exhibits 100% accuracy in categorizing data according to specific taxonomies and 88% accuracy in recommending products from a massive database of 50,000 items. Lamini Memory Tuning also supports complete automation, cost reduction, and faster development cycles, ensuring an efficient user experience.
Practical Implementations and Case Studies
A notable example is a Fortune 500 company that has successfully integrated Lamini's Memory Tuning, reporting a remarkable 95% accuracy in crucial applications. This marks a significant increase from the 50% accuracy achieved with previous methods. Memory Tuning has proven particularly effective in tasks needing accurate fact recall, such as converting language queries into SQL.
Lamini collaborates with select partners to deploy this advanced technique. The company offers optimized LLM tuning and inference solutions for enterprises, enabling factual LLM deployment in under ten minutes and compatibility across various platforms.
